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Published on: February 9, 2022
External validation can provide the clinician with IOS equations that best predict the risk of uncontrolled asthma in
Tricia Morphew1,2, Pornchai Tirakitsoontorn3,4, Stanley P Galant2,4
1Morphew Consulting, LLC, Bothell, Washington, USA.
Insights
Selecting the best impulse oscillometry (IOS) reference equations is crucial for identifying uncontrolled asthma in children. The Gochicoa-Rangel equations demonstrated superior accuracy in predicting uncontrolled asthma in pediatric patients.
Area of Science:
- Pediatric Pulmonology
- Respiratory Medicine
- Asthma Research
Background:
- Peripheral airway impairment (PAI) is linked to uncontrolled asthma in children.
- Clinicians lack standardized methods for selecting appropriate impulse oscillometry (IOS) reference equations.
- This study addresses the need for a practical approach to validate IOS equations for pediatric asthma assessment.
Purpose of the Study:
- To develop and assess a practical external validation method for selecting the best IOS reference equations.
- To identify which IOS reference equations best predict uncontrolled asthma in children.
- To provide clinicians with a reliable tool for personalized asthma management.
Main Methods:
- Post hoc analysis of data from a randomized controlled study (2016-2018) involving 227 children (ages 4-18) with moderate to severe asthma.
- Assessed discrimination and calibration performance of IOS equations using uncontrolled asthma as the outcome.
- Utilized accuracy, sensitivity, and specificity as primary performance indicators, with rank scores based on meeting thresholds for IOS metrics (R5, R5-R20, AX, X5).
Main Results:
- External validation ranked Gochicoa-Rangel equations highest (score=10), followed by Nowowiejska (score=9), Assumapcao (score=6), and Amra (score=2).
- Gochicoa-Rangel equations showed the best universal applicability, with accuracies of 73.1% (R5), 72.2% (R5-R20), 76.7% (AX), and 66.2% (X5).
Conclusions:
- External validation provides a practical method for clinicians to select optimal IOS predictive equations for pediatric asthma.
- The Gochicoa-Rangel equations are recommended for their superior performance in predicting uncontrolled asthma in children.
- This approach enhances the clinical utility of IOS in managing pediatric asthma effectively.
Background:
Peripheral airway impairment (PAI) has been shown to have a close association to risk of uncontrolled asthma in children. However, clear methods have not been established for the clinician to select impulse oscillometry (IOS) reference equations best suited for their population. Our study aimed to develop a practical external validation analytic approach for the clinician to determine which of the available reference equations best predicts uncontrolled asthma for their patients.
Methods:
This is a post hoc analyses of data collected at baseline in a randomized controlled study that occurred from March 2016 to 2018. The study population consisted of 227 children, ages 4-18 years, with moderate to severe asthma. Discrimination and calibration predictive performance of available and suitable IOS equations were assessed by using uncontrolled asthma as the criterion outcome. Discrimination statistics of accuracy, sensitivity, and specificity served as the primary performance indicators. Rank scores were determined by the number of acceptable limit thresholds met for these measures (≥60%, ≥50%, and ≥60%, respectively) across IOS metrics (R5, R5-R20, AX, and X5) resulting in a total possible score of 12.
Results:
External validity assessment determined the rank order of best to worst equations as being Gochicoa-Rangel (rank score = 10) > Nowowiejska (rank score = 9) > Assumapcao (rank score = 6) > Amra (rank score = 2). Gochicoa-Rangel reference equations provided the best option for universal application with accuracy of 73.1%, 72.2%, 76.7%, and 66.2% for R5, R5-R20, AX, and X5, respectively.
Conclusions:
External validation, particularly discrimination in asthmatic children, offers the clinician a practical approach to selecting the most suitable predictive equations for their patients.
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